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Escher.ipynb
- e_coli_core.jsonlast mo.
- Escher.ipynb31s ago
- iJO1366.jsonlast mo.
Max flux FUM_bw: 33.3000000000002 Min flux FUM_bw: -0.1
/home/cristian/.conda/envs/cnapy-1.2.4/lib/python3.10/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'. warn(f"Solver status is '{status}'.", UserWarning)
Max flux PDH_fw: 11.799999999999974 Min flux PDH_fw: -19.1
Max flux MDH_fw: 42.00000000000033 Min flux MDH_fw: -35.10000000000023
Max flux MDH_bw: 42.00000000000033 Min flux MDH_bw: -2.700000000000001
Max flux PPC_fw: 28.600000000000136 Min flux PPC_fw: -0.1
Max flux GLCptspp_fw: 0.0 Min flux GLCptspp_fw: 0.0
FUM_bw flux: 0.0 PPC_fw flux: 0.41250750342026987 GLCptspp_fw flux: 9.250127204153337 PDH_fw flux: 0.0 MDH_fw flux: 0.0 MDH_bw flux: 0.0
interactive(children=(FloatSlider(value=0.0, description='fum_bw', max=33.3, step=0.01), FloatSlider(value=0.4…
| Reaction identifier | MDH_fw |
| Name | Malate dehydrogenase |
| Memory address | 0x7fc7441222c0 |
| Stoichiometry |
0.00236811413346649 enzyme_pool + mal__L_c + nad_c --> h_c + nadh_c + oaa_c 0.00236811413346649 enzyme pool pseudometabolite + L-Malate + Nicotinamide adenine dinucleotide --> H+ + Nicotinamide adenine dinucleotide - reduced + Oxaloacetate |
| GPR | b3236 |
| Lower bound | 0.0 |
| Upper bound | 1000.0 |
| Reaction identifier | FUM_bw |
| Name | Fumarase |
| Memory address | 0x7f70707630a0 |
| Stoichiometry |
0.00694546169257057 enzyme_pool + mal__L_c --> fum_c + h2o_c 0.00694546169257057 enzyme pool pseudometabolite + L-Malate --> Fumarate + H2O H2O |
| GPR | b2929 or b1675 or b1612 or b4122 or b1611 |
| Lower bound | 0.0 |
| Upper bound | 1000.0 |
interactive(children=(FloatSlider(value=0.0, description='pdh', max=53.7, step=0.01), FloatSlider(value=0.0, d…
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
/home/cristian/.conda/envs/me25/lib/python3.12/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'. warn(f"Solver status is '{status}'.", UserWarning) /home/cristian/.conda/envs/me25/lib/python3.12/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'. warn(f"Solver status is '{status}'.", UserWarning)
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0.07646148405553027,
0.07578794362817462,
0.07511440320081997,
0.07444086277346593,
0.07376732234610524,
0.07309378191874939,
0.07242024149139978,
0.07174670106403949,
0.07107316063668465,
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0.06972607978197355,
0.06905253935461426,
0.06837899892725982,
0.06770545849990296,
0.06703191807254792,
0.06635837764519407,
0.06568483721783339,
0.06501129679047835,
0.0643377563631235,
0.06366421593576785,
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0.06231713508105372,
0.06164359465369606,
0.06097005422634947,
0.06029651379898878,
0.0596229733716287,
0.058949432944278284,
0.058275892516923035,
0.05760235208956275,
0.056928811662206695,
0.05625527123485165,
0.0555817308074968,
0.05490819038013733,
0.05423464995278228,
0.053561109525427836,
0.05288756909807682,
0.05221402867071069,
0.05154048824335685,
0.050866947816006036,
0.05019340738865159,
0.049519866961285464,
0.048846326533935454,
0.04817278610658786,
0.04749924567922536,
0.04682570525186124,
0.04615216482452534,
0.04547862439716042,
0.044805083969795295,
0.044131543542441254,
0.043458003115095276,
0.042784462687719675,
0.042110922260375105,
0.04143738183301986,
0.04076384140567468,
0.04009030097831057,
0.03941676055095512,
0.038743220123601686,
0.03806967969624442,
0.03739613926888071,
0.03672259884152486,
0.03604905841416759,
0.03537551798682322,
0.034701977559458703,
0.034028437132093185,
0.033354896704759295,
0.03268135627740465,
0.032007815850039124,
0.031334275422683674,
0.030660734995329834,
0.029987194567974385,
0.02931365414061712,
0.028640113713254416,
0.027966573285908236,
0.027293032858544522,
0.02661949243118585,
0.025945952003832616,
0.025272411576479182,
0.02459887114912373,
0.02392533072176949,
0.02325179029441303,
0.022578249867058184,
0.021904709439692863,
0.021231169012347284,
0.020557628584994454,
0.019884088157628727,
0.019210547730280733,
0.018537007302917827,
0.017863466875562378,
0.017189926448205717,
0.016516386020841604,
0.015842845593495424,
0.015169305166142994,
0.014495764738776059,
0.013822224311433304,
0.013148683884078458,
0.012475143456711522,
0.011801603029353855,
0.011128062602001226,
0.010454522174658268,
0.009780981747292744,
0.009107441319936287,
0.00843390089258144,
0.007760360465224177,
0.007086820037857646,
0.00641327961050562,
0.005739739183151782,
0.005066198755806809,
0.004392658328439471,
0.0037191179010850283,
0.00304557747374066,
0.0023720370463753367,
0.001698496619010014,
0.0010249561916644365,
0.0003514157643101952]}| NADH | H2 | Biomassa | |
|---|---|---|---|
| 0 | 3.990261 | 7.975509 | 0.001072 |
| 1 | 4.224465 | 8.427205 | 0.004645 |
| 2 | 4.458670 | 8.878902 | 0.008217 |
| 3 | 4.692874 | 9.330598 | 0.011790 |
| 4 | 4.927078 | 9.782295 | 0.015363 |
| ... | ... | ... | ... |
| 236 | 27.032296 | 0.089182 | 0.003046 |
| 237 | 27.087180 | 0.069459 | 0.002372 |
| 238 | 27.142065 | 0.049736 | 0.001698 |
| 239 | 27.196949 | 0.030013 | 0.001025 |
| 240 | 27.251833 | 0.010290 | 0.000351 |
241 rows × 3 columns
Read LP format model from file /tmp/tmp9p21pvv2.lp Reading time = 0.00 seconds : 310 rows, 1022 columns, 4870 nonzeros
| Biomassa | NADH | |
|---|---|---|
| SA | 0.617 | 28.3100 |
| SAn | 0.103 | 18.5300 |
| M | 26.070 | 0.0102 |
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.00000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.679557 | 0.000000 |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.09218 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.698335 | 0.005113 |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.00000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.835517 | 0.031742 |
| 3 | 11.157505 | 0.173741 | 2.504546 | 1.636492 | 3.173513 | 1.537021 | 3.805157 | 2.168665 | 1.798605 | 1.798605 | 0.00000 | 0.000000 | 0.000000 | 0.370528 | 0.608418 | 2.331004 | 0.932402 | 19.719451 | 0.000000 |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.00000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.306992 | 0.057947 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999995 | 10.860082 | 0.000000 | 0.671967 | 0.000000 | 0.000000 | 0.000000 | 3.352469 | 3.352469 | 0.099471 | 0.099471 | 0.00000 | 0.487509 | 0.487509 | 0.369804 | 0.369804 | 1.864803 | 2.536770 | 17.204439 | 0.000000 |
| 999996 | 8.480702 | 0.694965 | 2.138030 | 0.229919 | 2.151195 | 1.921276 | 1.725970 | 1.496051 | 1.798605 | 1.798605 | 0.00000 | 2.437544 | 2.437544 | 1.852641 | 2.548761 | 0.000000 | 0.466201 | 13.985866 | 0.000000 |
| 999997 | 8.778125 | 0.521223 | 3.237578 | 0.316884 | 1.469650 | 1.152766 | 1.725970 | 1.409086 | 0.883262 | 0.566378 | 0.00000 | 1.478769 | 1.445357 | 1.524338 | 1.479215 | 0.045123 | 0.000000 | 18.011220 | 0.063688 |
| 999998 | 9.967815 | 0.521223 | 1.771514 | 0.000000 | 1.636492 | 1.636492 | 1.084333 | 1.084333 | 1.232227 | 1.232227 | 0.00000 | 1.462527 | 1.462527 | 1.479215 | 1.479215 | 0.932402 | 2.703916 | 16.168368 | 0.000000 |
| 999999 | 10.562660 | 1.216188 | 0.671967 | 0.273401 | 2.194677 | 1.921276 | 3.352469 | 3.079067 | 0.556591 | 0.283189 | 0.00000 | 2.953313 | 2.925053 | 0.000000 | 0.000000 | 1.864803 | 1.864803 | 20.419944 | 0.053867 |
1000000 rows × 19 columns
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.00000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.679557 | 0.000000 | Geral |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.09218 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.698335 | 0.005113 | Geral |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.00000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.835517 | 0.031742 | Geral |
| 3 | 11.157505 | 0.173741 | 2.504546 | 1.636492 | 3.173513 | 1.537021 | 3.805157 | 2.168665 | 1.798605 | 1.798605 | 0.00000 | 0.000000 | 0.000000 | 0.370528 | 0.608418 | 2.331004 | 0.932402 | 19.719451 | 0.000000 | Geral |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.00000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.306992 | 0.057947 | Geral |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999995 | 10.860082 | 0.000000 | 0.671967 | 0.000000 | 0.000000 | 0.000000 | 3.352469 | 3.352469 | 0.099471 | 0.099471 | 0.00000 | 0.487509 | 0.487509 | 0.369804 | 0.369804 | 1.864803 | 2.536770 | 17.204439 | 0.000000 | Geral |
| 999996 | 8.480702 | 0.694965 | 2.138030 | 0.229919 | 2.151195 | 1.921276 | 1.725970 | 1.496051 | 1.798605 | 1.798605 | 0.00000 | 2.437544 | 2.437544 | 1.852641 | 2.548761 | 0.000000 | 0.466201 | 13.985866 | 0.000000 | Geral |
| 999997 | 8.778125 | 0.521223 | 3.237578 | 0.316884 | 1.469650 | 1.152766 | 1.725970 | 1.409086 | 0.883262 | 0.566378 | 0.00000 | 1.478769 | 1.445357 | 1.524338 | 1.479215 | 0.045123 | 0.000000 | 18.011220 | 0.063688 | Geral |
| 999998 | 9.967815 | 0.521223 | 1.771514 | 0.000000 | 1.636492 | 1.636492 | 1.084333 | 1.084333 | 1.232227 | 1.232227 | 0.00000 | 1.462527 | 1.462527 | 1.479215 | 1.479215 | 0.932402 | 2.703916 | 16.168368 | 0.000000 | Geral |
| 999999 | 10.562660 | 1.216188 | 0.671967 | 0.273401 | 2.194677 | 1.921276 | 3.352469 | 3.079067 | 0.556591 | 0.283189 | 0.00000 | 2.953313 | 2.925053 | 0.000000 | 0.000000 | 1.864803 | 1.864803 | 20.419944 | 0.053867 | Geral |
999751 rows × 20 columns
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 682 | 10.265237 | 1.563670 | 2.871062 | 0.142954 | 0.527209 | 0.384255 | 1.183804 | 1.040850 | 0.426143 | 0.283189 | 0.000000 | 0.519631 | 0.487509 | 0.370528 | 0.369804 | 4.195807 | 4.195807 | 24.417125 | 0.061228 |
| 1251 | 10.860082 | 1.389929 | 3.604094 | 0.360367 | 0.360367 | 0.000000 | 2.810302 | 2.449936 | 0.643556 | 0.283189 | 0.000000 | 1.971692 | 1.945435 | 0.035460 | 0.000000 | 4.195807 | 4.195807 | 24.657655 | 0.050049 |
| 11359 | 11.157505 | 1.042447 | 3.237578 | 0.490815 | 0.490815 | 0.000000 | 2.117313 | 1.626499 | 1.340382 | 0.849567 | 0.061453 | 0.492923 | 0.526793 | 0.407055 | 0.369804 | 4.195807 | 4.195807 | 24.592242 | 0.052577 |
| 11831 | 10.562660 | 1.389929 | 2.871062 | 0.309687 | 0.483726 | 0.174040 | 0.851853 | 0.542166 | 0.592876 | 0.283189 | 0.245814 | 0.000000 | 0.214524 | 1.151667 | 1.109411 | 3.729606 | 3.729606 | 24.214517 | 0.059641 |
| 13024 | 10.562660 | 1.563670 | 3.604094 | 0.099471 | 0.099471 | 0.000000 | 1.183804 | 1.084333 | 0.949038 | 0.849567 | 0.000000 | 0.518868 | 0.487509 | 0.412153 | 0.369804 | 3.263405 | 3.263405 | 24.057084 | 0.059772 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 967341 | 11.157505 | 1.216188 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 1.531665 | 1.084333 | 0.730521 | 0.283189 | 0.153634 | 0.492923 | 0.614988 | 0.412436 | 0.369804 | 2.797205 | 3.263405 | 24.006515 | 0.060172 |
| 973918 | 10.562660 | 1.042447 | 3.237578 | 0.192313 | 0.960823 | 0.768510 | 0.192313 | 0.000000 | 0.192313 | 0.000000 | 0.122907 | 0.492923 | 0.581830 | 0.785524 | 0.739607 | 3.729606 | 4.195807 | 24.080630 | 0.064808 |
| 977070 | 10.265237 | 1.563670 | 3.237578 | 0.099471 | 1.252237 | 1.152766 | 0.099471 | 0.000000 | 0.949038 | 0.849567 | 0.000000 | 0.518514 | 0.487509 | 0.000000 | 0.000000 | 4.195807 | 4.195807 | 24.469771 | 0.059098 |
| 978778 | 11.157505 | 1.389929 | 3.604094 | 0.338586 | 0.867981 | 0.529395 | 1.965085 | 1.626499 | 0.338586 | 0.000000 | 0.276540 | 0.492923 | 0.739069 | 0.041047 | 0.000000 | 2.838252 | 2.797205 | 24.245067 | 0.057935 |
| 988765 | 10.265237 | 1.563670 | 2.504546 | 0.316884 | 0.701139 | 0.384255 | 0.316884 | 0.000000 | 0.600073 | 0.283189 | 0.245814 | 0.000000 | 0.213570 | 0.783151 | 0.739607 | 3.773150 | 3.729606 | 24.031348 | 0.061459 |
249 rows × 19 columns
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.000000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.679557 | 0.00000 | Geral |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.092180 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.698335 | 0.00511 | Geral |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.000000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.835517 | 0.03174 | Geral |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.000000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.306992 | 0.05795 | Geral |
| 5 | 8.480702 | 1.389929 | 2.504546 | 0.316884 | 0.316884 | 0.000000 | 0.316884 | 0.000000 | 1.232227 | 1.132756 | 0.030727 | 0.492923 | 0.492814 | 1.520858 | 1.479215 | 1.398602 | 1.398602 | 19.374862 | 0.05878 | Geral |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 991797 | 10.265237 | 1.216188 | 2.138030 | 1.636492 | 3.173513 | 1.537021 | 1.636492 | 0.000000 | 2.081794 | 1.415945 | 0.000000 | 0.006332 | 0.000000 | 0.748159 | 0.739607 | 2.843997 | 1.864803 | 20.662284 | 0.01207 | Geral |
| 993694 | 8.778125 | 0.347482 | 1.038482 | 0.099471 | 0.483726 | 0.384255 | 0.641637 | 0.542166 | 1.232227 | 1.132756 | 0.000857 | 0.047451 | 0.000000 | 0.000000 | 0.669568 | 0.262834 | 0.932402 | 18.154520 | 0.09208 | Geral |
| 996912 | 10.265237 | 1.042447 | 3.604094 | 1.725970 | 2.110225 | 0.384255 | 2.810302 | 1.084333 | 0.382660 | 0.381076 | 0.000000 | 0.975794 | 0.975018 | 0.001048 | 0.000000 | 0.000000 | 0.000000 | 17.914036 | 0.00148 | Geral |
| 997936 | 10.562660 | 1.042447 | 2.504546 | 1.636492 | 3.557768 | 1.921276 | 1.636492 | 0.000000 | 0.949038 | 0.566378 | 0.000000 | 0.978073 | 0.975018 | 1.113537 | 1.109411 | 3.729606 | 3.263405 | 21.522768 | 0.00582 | Geral |
| 999437 | 9.967815 | 1.216188 | 3.604094 | 2.020747 | 3.557768 | 1.537021 | 4.731578 | 2.710831 | 2.587125 | 0.566378 | 0.215087 | 0.492923 | 0.707136 | 1.480396 | 1.479215 | 2.797205 | 2.797205 | 20.475337 | 0.00167 | Geral |
9079 rows × 20 columns
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 682 | 10.265237 | 1.563670 | 2.871062 | 0.142954 | 0.527209 | 0.384255 | 1.183804 | 1.040850 | 0.426143 | 0.283189 | 0.000000 | 0.519631 | 0.487509 | 0.370528 | 0.369804 | 4.195807 | 4.195807 | 24.42 | 0.061228 | Melhores |
| 1251 | 10.860082 | 1.389929 | 3.604094 | 0.360367 | 0.360367 | 0.000000 | 2.810302 | 2.449936 | 0.643556 | 0.283189 | 0.000000 | 1.971692 | 1.945435 | 0.035460 | 0.000000 | 4.195807 | 4.195807 | 24.66 | 0.050049 | Melhores |
| 11359 | 11.157505 | 1.042447 | 3.237578 | 0.490815 | 0.490815 | 0.000000 | 2.117313 | 1.626499 | 1.340382 | 0.849567 | 0.061453 | 0.492923 | 0.526793 | 0.407055 | 0.369804 | 4.195807 | 4.195807 | 24.59 | 0.052577 | Melhores |
| 11831 | 10.562660 | 1.389929 | 2.871062 | 0.309687 | 0.483726 | 0.174040 | 0.851853 | 0.542166 | 0.592876 | 0.283189 | 0.245814 | 0.000000 | 0.214524 | 1.151667 | 1.109411 | 3.729606 | 3.729606 | 24.21 | 0.059641 | Melhores |
| 13024 | 10.562660 | 1.563670 | 3.604094 | 0.099471 | 0.099471 | 0.000000 | 1.183804 | 1.084333 | 0.949038 | 0.849567 | 0.000000 | 0.518868 | 0.487509 | 0.412153 | 0.369804 | 3.263405 | 3.263405 | 24.06 | 0.059772 | Melhores |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 875919 | 10.265237 | 0.868706 | 3.237578 | 0.099471 | 0.867981 | 0.768510 | 0.099471 | 0.000000 | 0.099471 | 0.000000 | 0.000000 | 0.037844 | 0.000000 | 0.000000 | 0.000000 | 4.195807 | 4.195807 | 24.38 | 0.072134 | Melhores |
| 918573 | 10.860082 | 1.389929 | 3.237578 | 0.447332 | 0.447332 | 0.000000 | 3.352469 | 2.905137 | 0.730521 | 0.283189 | 0.000000 | 0.027327 | 0.000000 | 0.741057 | 0.749979 | 4.186884 | 4.195807 | 24.61 | 0.052087 | Melhores |
| 945138 | 11.157505 | 1.042447 | 3.237578 | 0.099471 | 0.099471 | 0.000000 | 0.099471 | 0.000000 | 0.382660 | 0.283189 | 0.000000 | 1.980634 | 1.950035 | 0.370528 | 0.370528 | 4.195807 | 4.195807 | 24.62 | 0.058324 | Melhores |
| 958074 | 11.157505 | 1.042447 | 3.604094 | 0.447332 | 1.600098 | 1.152766 | 0.641637 | 0.194305 | 0.730521 | 0.283189 | 0.000000 | 0.031451 | 0.000000 | 0.412277 | 0.369804 | 3.305879 | 3.263405 | 24.25 | 0.059948 | Melhores |
| 958825 | 10.562660 | 1.042447 | 3.604094 | 0.192834 | 0.192834 | 0.000000 | 0.735000 | 0.542166 | 0.476023 | 0.283189 | 0.122907 | 0.492923 | 0.581575 | 0.046261 | 0.000000 | 4.195807 | 4.195807 | 24.84 | 0.065294 | Melhores |
78 rows × 20 columns
<ggplot: (640 x 480)>
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | Label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.000000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.679557 | 0.000000 | Geral |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.092180 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.698335 | 0.005110 | Geral |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.000000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.835517 | 0.031740 | Geral |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.000000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.306992 | 0.057950 | Geral |
| 5 | 8.480702 | 1.389929 | 2.504546 | 0.316884 | 0.316884 | 0.000000 | 0.316884 | 0.000000 | 1.232227 | 1.132756 | 0.030727 | 0.492923 | 0.492814 | 1.520858 | 1.479215 | 1.398602 | 1.398602 | 19.374862 | 0.058780 | Geral |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 875919 | 10.265237 | 0.868706 | 3.237578 | 0.099471 | 0.867981 | 0.768510 | 0.099471 | 0.000000 | 0.099471 | 0.000000 | 0.000000 | 0.037844 | 0.000000 | 0.000000 | 0.000000 | 4.195807 | 4.195807 | 24.380000 | 0.072134 | Melhores |
| 918573 | 10.860082 | 1.389929 | 3.237578 | 0.447332 | 0.447332 | 0.000000 | 3.352469 | 2.905137 | 0.730521 | 0.283189 | 0.000000 | 0.027327 | 0.000000 | 0.741057 | 0.749979 | 4.186884 | 4.195807 | 24.610000 | 0.052087 | Melhores |
| 945138 | 11.157505 | 1.042447 | 3.237578 | 0.099471 | 0.099471 | 0.000000 | 0.099471 | 0.000000 | 0.382660 | 0.283189 | 0.000000 | 1.980634 | 1.950035 | 0.370528 | 0.370528 | 4.195807 | 4.195807 | 24.620000 | 0.058324 | Melhores |
| 958074 | 11.157505 | 1.042447 | 3.604094 | 0.447332 | 1.600098 | 1.152766 | 0.641637 | 0.194305 | 0.730521 | 0.283189 | 0.000000 | 0.031451 | 0.000000 | 0.412277 | 0.369804 | 3.305879 | 3.263405 | 24.250000 | 0.059948 | Melhores |
| 958825 | 10.562660 | 1.042447 | 3.604094 | 0.192834 | 0.192834 | 0.000000 | 0.735000 | 0.542166 | 0.476023 | 0.283189 | 0.122907 | 0.492923 | 0.581575 | 0.046261 | 0.000000 | 4.195807 | 4.195807 | 24.840000 | 0.065294 | Melhores |
9157 rows × 20 columns
/home/cristian/.conda/envs/me25/lib/python3.12/site-packages/plotnine/ggplot.py:630: PlotnineWarning: Saving 14 x 6 in image. /home/cristian/.conda/envs/me25/lib/python3.12/site-packages/plotnine/ggplot.py:631: PlotnineWarning: Filename: scatter.png
| ano_ingresso | periodo_ingresso | curso | nivel_curso | descricao_cor_raca | descricao_quota | total | Unnamed: 7 | Unnamed: 8 | |
|---|---|---|---|---|---|---|---|---|---|
| 583 | 2025 | 1 | BA | D | BRANCA | Não Cotista | 3 | NaN | NaN |
| 584 | 2025 | 1 | BA | D | PARDA | Não Cotista | 1 | NaN | NaN |
| 585 | 2025 | 1 | BA | D | PRETA | Autodeclarado preto ou pardo | 1 | NaN | NaN |
| 600 | 2025 | 1 | BA | M | BRANCA | Não Cotista | 7 | NaN | NaN |
| 601 | 2025 | 1 | BA | M | INDÍGENA | Não Cotista | 1 | NaN | NaN |
| 602 | 2025 | 1 | BA | M | PARDA | Autodeclarado preto ou pardo | 1 | NaN | NaN |
| 603 | 2025 | 1 | BA | M | PARDA | Não Cotista | 2 | NaN | NaN |
| 604 | 2025 | 1 | BA | M | PRETA | Autodeclarado preto ou pardo | 1 | NaN | NaN |
| 621 | 2025 | 2 | BA | D | BRANCA | Não Cotista | 1 | NaN | NaN |
| 633 | 2025 | 2 | BA | M | BRANCA | Não Cotista | 1 | NaN | NaN |
| ano_ingresso | periodo_ingresso | curso | nivel_curso | descricao_cor_raca | descricao_quota | total | Unnamed: 7 | Unnamed: 8 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015 | 1 | BFM | D | BRANCA | Não Cotista | 7 | NaN | NaN |
| 1 | 2015 | 1 | BFM | D | NAO DECLARADO | Não Cotista | 8 | NaN | NaN |
| 2 | 2015 | 1 | BCE | D | BRANCA | Não Cotista | 4 | NaN | NaN |
| 3 | 2015 | 1 | BCE | D | NAO DECLARADO | Não Cotista | 3 | NaN | NaN |
| 4 | 2015 | 1 | GBM | D | BRANCA | Não Cotista | 9 | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 631 | 2025 | 2 | BV | M | PARDA | Autodeclarado preto ou pardo | 1 | NaN | NaN |
| 632 | 2025 | 2 | BV | M | PARDA | Não Cotista | 1 | NaN | NaN |
| 633 | 2025 | 2 | BA | M | BRANCA | Não Cotista | 1 | NaN | NaN |
| 634 | 2025 | 2 | BMM | M | BRANCA | Não Cotista | 7 | NaN | NaN |
| 635 | 2025 | 2 | BMM | M | PARDA | Não Cotista | 1 | NaN | NaN |
636 rows × 9 columns
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 13 | 8 | 0 | 0 | 1 | 22 |
| 2016 | 14 | 5 | 2 | 0 | 0 | 21 |
| 2017 | 23 | 5 | 3 | 0 | 0 | 31 |
| 2018 | 17 | 5 | 4 | 0 | 1 | 27 |
| 2019 | 15 | 0 | 2 | 0 | 2 | 19 |
| 2020 | 9 | 0 | 7 | 0 | 0 | 16 |
| 2021 | 13 | 2 | 1 | 0 | 0 | 16 |
| 2022 | 9 | 0 | 4 | 0 | 0 | 13 |
| 2023 | 4 | 1 | 3 | 0 | 0 | 8 |
| 2024 | 7 | 0 | 2 | 0 | 1 | 10 |
| 2025 | 12 | 0 | 6 | 1 | 0 | 19 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 8 | 4 | 0 | 0 | 0 | 12 |
| 2015.2 | 5 | 4 | 0 | 0 | 1 | 10 |
| 2016.1 | 9 | 3 | 1 | 0 | 0 | 13 |
| 2016.2 | 5 | 2 | 1 | 0 | 0 | 8 |
| 2017.1 | 12 | 2 | 1 | 0 | 0 | 15 |
| 2017.2 | 11 | 3 | 2 | 0 | 0 | 16 |
| 2018.1 | 16 | 5 | 3 | 0 | 1 | 25 |
| 2018.2 | 1 | 0 | 1 | 0 | 0 | 2 |
| 2019.1 | 11 | 0 | 0 | 0 | 1 | 12 |
| 2019.2 | 4 | 0 | 2 | 0 | 1 | 7 |
| 2020.1 | 7 | 0 | 5 | 0 | 0 | 12 |
| 2020.2 | 2 | 0 | 2 | 0 | 0 | 4 |
| 2021.1 | 13 | 2 | 1 | 0 | 0 | 16 |
| 2021.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022.1 | 3 | 0 | 4 | 0 | 0 | 7 |
| 2022.2 | 6 | 0 | 0 | 0 | 0 | 6 |
| 2023.1 | 4 | 1 | 2 | 0 | 0 | 7 |
| 2023.2 | 0 | 0 | 1 | 0 | 0 | 1 |
| 2024.1 | 6 | 0 | 2 | 0 | 1 | 9 |
| 2024.2 | 1 | 0 | 0 | 0 | 0 | 1 |
| 2025.1 | 10 | 0 | 6 | 1 | 0 | 17 |
| 2025.2 | 2 | 0 | 0 | 0 | 0 | 2 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 3 | 15 | 0 | 0 | 0 | 18 |
| 2016 | 12 | 9 | 5 | 1 | 1 | 28 |
| 2017 | 15 | 8 | 2 | 0 | 0 | 25 |
| 2018 | 9 | 3 | 1 | 1 | 0 | 14 |
| 2019 | 19 | 1 | 3 | 0 | 1 | 24 |
| 2020 | 4 | 4 | 3 | 0 | 0 | 11 |
| 2021 | 12 | 7 | 6 | 0 | 0 | 25 |
| 2022 | 11 | 1 | 4 | 0 | 0 | 16 |
| 2023 | 4 | 4 | 5 | 0 | 1 | 14 |
| 2024 | 13 | 3 | 5 | 1 | 0 | 22 |
| 2025 | 15 | 1 | 9 | 0 | 1 | 26 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 3 | 7 | 0 | 0 | 0 | 10 |
| 2015.2 | 0 | 8 | 0 | 0 | 0 | 8 |
| 2016.1 | 8 | 6 | 4 | 1 | 1 | 20 |
| 2016.2 | 4 | 3 | 1 | 0 | 0 | 8 |
| 2017.1 | 14 | 8 | 2 | 0 | 0 | 24 |
| 2017.2 | 1 | 0 | 0 | 0 | 0 | 1 |
| 2018.1 | 7 | 2 | 1 | 0 | 0 | 10 |
| 2018.2 | 2 | 1 | 0 | 1 | 0 | 4 |
| 2019.1 | 12 | 0 | 1 | 0 | 1 | 14 |
| 2019.2 | 7 | 1 | 2 | 0 | 0 | 10 |
| 2020.1 | 4 | 4 | 3 | 0 | 0 | 11 |
| 2020.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2021.1 | 5 | 3 | 3 | 0 | 0 | 11 |
| 2021.2 | 7 | 4 | 3 | 0 | 0 | 14 |
| 2022.1 | 9 | 1 | 1 | 0 | 0 | 11 |
| 2022.2 | 2 | 0 | 3 | 0 | 0 | 5 |
| 2023.1 | 1 | 0 | 4 | 0 | 1 | 6 |
| 2023.2 | 3 | 4 | 1 | 0 | 0 | 8 |
| 2024.1 | 6 | 2 | 2 | 1 | 0 | 11 |
| 2024.2 | 7 | 1 | 3 | 0 | 0 | 11 |
| 2025.1 | 11 | 1 | 6 | 0 | 1 | 19 |
| 2025.2 | 4 | 0 | 3 | 0 | 0 | 7 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 30 | 24 | 7 | 0 | 2 | 63 |
| 2016 | 23 | 17 | 8 | 0 | 2 | 50 |
| 2017 | 37 | 13 | 10 | 0 | 2 | 62 |
| 2018 | 45 | 15 | 11 | 1 | 3 | 75 |
| 2019 | 40 | 13 | 12 | 0 | 1 | 66 |
| 2020 | 38 | 9 | 12 | 1 | 1 | 61 |
| 2021 | 27 | 3 | 17 | 0 | 1 | 48 |
| 2022 | 35 | 4 | 6 | 0 | 0 | 45 |
| 2023 | 24 | 4 | 12 | 0 | 1 | 41 |
| 2024 | 33 | 6 | 6 | 0 | 0 | 45 |
| 2025 | 37 | 6 | 21 | 0 | 1 | 65 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 26 | 13 | 6 | 0 | 2 | 47 |
| 2015.2 | 4 | 11 | 1 | 0 | 0 | 16 |
| 2016.1 | 18 | 10 | 5 | 0 | 2 | 35 |
| 2016.2 | 5 | 7 | 3 | 0 | 0 | 15 |
| 2017.1 | 32 | 8 | 6 | 0 | 1 | 47 |
| 2017.2 | 5 | 5 | 4 | 0 | 1 | 15 |
| 2018.1 | 33 | 11 | 8 | 1 | 1 | 54 |
| 2018.2 | 12 | 4 | 3 | 0 | 2 | 21 |
| 2019.1 | 31 | 8 | 11 | 0 | 0 | 50 |
| 2019.2 | 9 | 5 | 1 | 0 | 1 | 16 |
| 2020.1 | 22 | 7 | 6 | 1 | 1 | 37 |
| 2020.2 | 15 | 2 | 6 | 0 | 0 | 23 |
| 2021.1 | 16 | 3 | 12 | 0 | 1 | 32 |
| 2021.2 | 11 | 0 | 5 | 0 | 0 | 16 |
| 2022.1 | 20 | 4 | 2 | 0 | 0 | 26 |
| 2022.2 | 15 | 0 | 4 | 0 | 0 | 19 |
| 2023.1 | 14 | 4 | 6 | 0 | 1 | 25 |
| 2023.2 | 10 | 0 | 6 | 0 | 0 | 16 |
| 2024.1 | 18 | 4 | 2 | 0 | 0 | 24 |
| 2024.2 | 15 | 2 | 4 | 0 | 0 | 21 |
| 2025.1 | 24 | 3 | 15 | 0 | 1 | 43 |
| 2025.2 | 13 | 3 | 6 | 0 | 0 | 22 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 30 | 24 | 7 | 0 | 2 | 63 |
| 2016 | 23 | 17 | 8 | 0 | 2 | 50 |
| 2017 | 37 | 13 | 10 | 0 | 2 | 62 |
| 2018 | 45 | 15 | 11 | 1 | 3 | 75 |
| 2019 | 40 | 13 | 12 | 0 | 1 | 66 |
| 2020 | 38 | 9 | 12 | 1 | 1 | 61 |
| 2021 | 27 | 3 | 17 | 0 | 1 | 48 |
| 2022 | 35 | 4 | 6 | 0 | 0 | 45 |
| 2023 | 24 | 4 | 12 | 0 | 1 | 41 |
| 2024 | 33 | 6 | 6 | 0 | 0 | 45 |
| 2025 | 37 | 6 | 21 | 0 | 1 | 65 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 26 | 13 | 6 | 0 | 2 | 47 |
| 2015.2 | 4 | 11 | 1 | 0 | 0 | 16 |
| 2016.1 | 18 | 10 | 5 | 0 | 2 | 35 |
| 2016.2 | 5 | 7 | 3 | 0 | 0 | 15 |
| 2017.1 | 32 | 8 | 6 | 0 | 1 | 47 |
| 2017.2 | 5 | 5 | 4 | 0 | 1 | 15 |
| 2018.1 | 33 | 11 | 8 | 1 | 1 | 54 |
| 2018.2 | 12 | 4 | 3 | 0 | 2 | 21 |
| 2019.1 | 31 | 8 | 11 | 0 | 0 | 50 |
| 2019.2 | 9 | 5 | 1 | 0 | 1 | 16 |
| 2020.1 | 22 | 7 | 6 | 1 | 1 | 37 |
| 2020.2 | 15 | 2 | 6 | 0 | 0 | 23 |
| 2021.1 | 16 | 3 | 12 | 0 | 1 | 32 |
| 2021.2 | 11 | 0 | 5 | 0 | 0 | 16 |
| 2022.1 | 20 | 4 | 2 | 0 | 0 | 26 |
| 2022.2 | 15 | 0 | 4 | 0 | 0 | 19 |
| 2023.1 | 14 | 4 | 6 | 0 | 1 | 25 |
| 2023.2 | 9 | 0 | 6 | 0 | 0 | 15 |
| 2024.1 | 18 | 4 | 2 | 0 | 0 | 24 |
| 2024.2 | 15 | 2 | 4 | 0 | 0 | 21 |
| 2025.1 | 24 | 3 | 15 | 0 | 1 | 43 |
| 2025.2 | 13 | 3 | 6 | 0 | 0 | 22 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2022 | 106 | 26 | 21 | 0 | 2 | 155 |
| 2023 | 18 | 2 | 10 | 1 | 0 | 31 |
| 2024 | 31 | 5 | 9 | 0 | 2 | 47 |
| 2025 | 46 | 2 | 13 | 0 | 2 | 63 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 16 | 16 | 3 | 0 | 0 | 35 |
| 2016 | 17 | 7 | 3 | 0 | 0 | 27 |
| 2017 | 16 | 3 | 4 | 0 | 0 | 23 |
| 2018 | 15 | 3 | 6 | 0 | 0 | 24 |
| 2019 | 17 | 7 | 4 | 0 | 0 | 28 |
| 2020 | 18 | 4 | 1 | 0 | 0 | 23 |
| 2021 | 8 | 4 | 4 | 0 | 0 | 16 |
| 2022 | 12 | 0 | 1 | 0 | 0 | 13 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 21 | 11 | 4 | 0 | 0 | 36 |
| 2016 | 10 | 12 | 4 | 0 | 0 | 26 |
| 2017 | 19 | 5 | 3 | 0 | 0 | 27 |
| 2018 | 20 | 6 | 3 | 0 | 1 | 30 |
| 2019 | 21 | 4 | 2 | 0 | 1 | 28 |
| 2020 | 19 | 4 | 6 | 0 | 1 | 30 |
| 2021 | 19 | 3 | 5 | 0 | 1 | 28 |
| 2022 | 7 | 0 | 2 | 0 | 0 | 9 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 10 | 10 | 1 | 0 | 1 | 22 |
| 2016 | 13 | 5 | 1 | 0 | 0 | 19 |
| 2017 | 16 | 5 | 1 | 0 | 0 | 22 |
| 2018 | 12 | 2 | 3 | 0 | 0 | 17 |
| 2019 | 13 | 4 | 9 | 0 | 0 | 26 |
| 2020 | 11 | 1 | 3 | 0 | 0 | 15 |
| 2021 | 19 | 0 | 6 | 0 | 1 | 26 |
| 2022 | 13 | 2 | 2 | 0 | 0 | 17 |
| 2023 | 15 | 2 | 7 | 0 | 0 | 24 |
| 2024 | 17 | 2 | 6 | 0 | 0 | 25 |
| 2025 | 19 | 0 | 5 | 0 | 0 | 24 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 6 | 9 | 1 | 0 | 1 | 17 |
| 2015.2 | 4 | 1 | 0 | 0 | 0 | 5 |
| 2016.1 | 4 | 3 | 1 | 0 | 0 | 8 |
| 2016.2 | 9 | 2 | 0 | 0 | 0 | 11 |
| 2017.1 | 8 | 2 | 1 | 0 | 0 | 11 |
| 2017.2 | 8 | 3 | 0 | 0 | 0 | 11 |
| 2018.1 | 9 | 2 | 1 | 0 | 0 | 12 |
| 2018.2 | 3 | 0 | 2 | 0 | 0 | 5 |
| 2019.1 | 10 | 2 | 7 | 0 | 0 | 19 |
| 2019.2 | 3 | 2 | 2 | 0 | 0 | 7 |
| 2020.1 | 10 | 1 | 3 | 0 | 0 | 14 |
| 2020.2 | 1 | 0 | 0 | 0 | 0 | 1 |
| 2021.1 | 14 | 0 | 4 | 0 | 1 | 19 |
| 2021.2 | 5 | 0 | 2 | 0 | 0 | 7 |
| 2022.1 | 11 | 2 | 2 | 0 | 0 | 15 |
| 2022.2 | 2 | 0 | 0 | 0 | 0 | 2 |
| 2023.1 | 9 | 2 | 7 | 0 | 0 | 18 |
| 2023.2 | 6 | 0 | 0 | 0 | 0 | 6 |
| 2024.1 | 16 | 2 | 6 | 0 | 0 | 24 |
| 2024.2 | 1 | 0 | 0 | 0 | 0 | 1 |
| 2025.1 | 17 | 0 | 2 | 0 | 0 | 19 |
| 2025.2 | 2 | 0 | 3 | 0 | 0 | 5 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2017 | 2 | 18 | 0 | 0 | 0 | 20 |
| 2018 | 11 | 6 | 3 | 0 | 0 | 20 |
| 2020 | 15 | 2 | 2 | 0 | 1 | 20 |
| 2022 | 13 | 5 | 2 | 0 | 0 | 20 |
| 2023 | 13 | 2 | 5 | 0 | 0 | 20 |
| 2024 | 10 | 1 | 7 | 0 | 1 | 19 |
| 2025 | 12 | 1 | 2 | 0 | 0 | 15 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2015.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2016.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2016.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2017.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2017.2 | 2 | 18 | 0 | 0 | 0 | 20 |
| 2018.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2018.2 | 11 | 6 | 3 | 0 | 0 | 20 |
| 2019.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2019.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020.1 | 15 | 2 | 2 | 0 | 1 | 20 |
| 2020.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2021.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2021.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2023.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2023.2 | 13 | 2 | 5 | 0 | 0 | 20 |
| 2024.1 | 10 | 1 | 7 | 0 | 1 | 19 |
| 2024.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2025.1 | 12 | 1 | 2 | 0 | 0 | 15 |
| 2025.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2017 | 2 | 18 | 0 | 0 | 0 | 20 |
| 2018 | 11 | 6 | 3 | 0 | 0 | 20 |
| 2020 | 15 | 2 | 2 | 0 | 1 | 20 |
| 2022 | 13 | 5 | 2 | 0 | 0 | 20 |
| 2023 | 13 | 2 | 5 | 0 | 0 | 20 |
| 2024 | 10 | 1 | 7 | 0 | 1 | 19 |
| 2025 | 12 | 1 | 2 | 0 | 0 | 15 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2015.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2016.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2016.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2017.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2017.2 | 2 | 18 | 0 | 0 | 0 | 20 |
| 2018.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2018.2 | 11 | 6 | 3 | 0 | 0 | 20 |
| 2019.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2019.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020.1 | 15 | 2 | 2 | 0 | 1 | 20 |
| 2020.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2021.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2021.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2023.1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2023.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2024.1 | 10 | 1 | 7 | 0 | 1 | 19 |
| 2024.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2025.1 | 12 | 1 | 2 | 0 | 0 | 15 |
| 2025.2 | 0 | 0 | 0 | 0 | 0 | 0 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2016 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2017 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2018 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2019 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2021 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2022 | 206.0 | 38.0 | 42.0 | 0.0 | 2.0 | 288.0 |
| 2023 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2024 | NaN | NaN | NaN | NaN | NaN | NaN |
| 2025 | NaN | NaN | NaN | NaN | NaN | NaN |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 71 | 49 | 10 | 0 | 3 | 133 |
| 2015.2 | 22 | 35 | 5 | 0 | 1 | 63 |
| 2016.1 | 58 | 34 | 16 | 1 | 3 | 112 |
| 2016.2 | 31 | 21 | 7 | 0 | 0 | 59 |
| 2017.1 | 91 | 25 | 16 | 0 | 1 | 133 |
| 2017.2 | 37 | 32 | 7 | 0 | 1 | 77 |
| 2018.1 | 87 | 25 | 20 | 1 | 2 | 135 |
| 2018.2 | 42 | 15 | 11 | 1 | 3 | 72 |
| 2019.1 | 84 | 17 | 22 | 0 | 3 | 126 |
| 2019.2 | 41 | 12 | 10 | 0 | 2 | 65 |
| 2020.1 | 79 | 19 | 25 | 1 | 2 | 126 |
| 2020.2 | 34 | 5 | 9 | 0 | 1 | 49 |
| 2021.1 | 65 | 14 | 28 | 0 | 3 | 110 |
| 2021.2 | 33 | 5 | 11 | 0 | 0 | 49 |
| 2022.1 | 62 | 7 | 12 | 0 | 0 | 81 |
| 2022.2 | 131 | 26 | 28 | 0 | 2 | 187 |
| 2023.1 | 41 | 7 | 22 | 1 | 2 | 73 |
| 2023.2 | 37 | 8 | 20 | 0 | 0 | 65 |
| 2024.1 | 83 | 13 | 22 | 1 | 4 | 123 |
| 2024.2 | 28 | 4 | 13 | 0 | 0 | 45 |
| 2025.1 | 110 | 6 | 40 | 1 | 4 | 161 |
| 2025.2 | 31 | 4 | 16 | 0 | 0 | 51 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015.1 | 53.383459 | 49 | 7.518797 | 0 | 3 | 133 |
| 2015.2 | 34.920635 | 35 | 7.936508 | 0 | 1 | 63 |
| 2016.1 | 51.785714 | 34 | 14.285714 | 1 | 3 | 112 |
| 2016.2 | 52.542373 | 21 | 11.864407 | 0 | 0 | 59 |
| 2017.1 | 68.421053 | 25 | 12.030075 | 0 | 1 | 133 |
| 2017.2 | 48.051948 | 32 | 9.090909 | 0 | 1 | 77 |
| 2018.1 | 64.444444 | 25 | 14.814815 | 1 | 2 | 135 |
| 2018.2 | 58.333333 | 15 | 15.277778 | 1 | 3 | 72 |
| 2019.1 | 66.666667 | 17 | 17.460317 | 0 | 3 | 126 |
| 2019.2 | 63.076923 | 12 | 15.384615 | 0 | 2 | 65 |
| 2020.1 | 62.698413 | 19 | 19.841270 | 1 | 2 | 126 |
| 2020.2 | 69.387755 | 5 | 18.367347 | 0 | 1 | 49 |
| 2021.1 | 59.090909 | 14 | 25.454545 | 0 | 3 | 110 |
| 2021.2 | 67.346939 | 5 | 22.448980 | 0 | 0 | 49 |
| 2022.1 | 76.543210 | 7 | 14.814815 | 0 | 0 | 81 |
| 2022.2 | 70.053476 | 26 | 14.973262 | 0 | 2 | 187 |
| 2023.1 | 56.164384 | 7 | 30.136986 | 1 | 2 | 73 |
| 2023.2 | 52.272727 | 6 | 34.090909 | 0 | 0 | 44 |
| 2024.1 | 67.479675 | 13 | 17.886179 | 1 | 4 | 123 |
| 2024.2 | 62.222222 | 4 | 28.888889 | 0 | 0 | 45 |
| 2025.1 | 68.322981 | 6 | 24.844720 | 1 | 4 | 161 |
| 2025.2 | 60.784314 | 4 | 31.372549 | 0 | 0 | 51 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 57 | 16 | 8 | 0 | 3 | 84 |
| 2016 | 60 | 16 | 12 | 1 | 3 | 92 |
| 2017 | 78 | 32 | 12 | 0 | 0 | 122 |
| 2018 | 75 | 17 | 18 | 0 | 4 | 114 |
| 2019 | 61 | 13 | 11 | 0 | 4 | 89 |
| 2020 | 54 | 11 | 18 | 0 | 1 | 84 |
| 2021 | 43 | 9 | 18 | 0 | 0 | 70 |
| 2022 | 112 | 15 | 24 | 0 | 0 | 151 |
| 2023 | 46 | 8 | 21 | 1 | 2 | 78 |
| 2024 | 58 | 6 | 18 | 0 | 2 | 84 |
| 2025 | 89 | 2 | 25 | 1 | 2 | 119 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 57 | 16 | 8 | 0 | 3 | 68 |
| 2016 | 60 | 16 | 12 | 1 | 3 | 76 |
| 2017 | 78 | 32 | 12 | 0 | 0 | 90 |
| 2018 | 75 | 17 | 18 | 0 | 4 | 97 |
| 2019 | 61 | 13 | 11 | 0 | 4 | 76 |
| 2020 | 54 | 11 | 18 | 0 | 1 | 73 |
| 2021 | 43 | 9 | 18 | 0 | 0 | 61 |
| 2022 | 112 | 15 | 24 | 0 | 0 | 136 |
| 2023 | 46 | 8 | 21 | 1 | 2 | 70 |
| 2024 | 58 | 6 | 18 | 0 | 2 | 78 |
| 2025 | 89 | 2 | 25 | 1 | 2 | 117 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 57 | 16 | 9.523810 | 0 | 3 | 84 |
| 2016 | 60 | 16 | 13.043478 | 1 | 3 | 92 |
| 2017 | 78 | 32 | 9.836066 | 0 | 0 | 122 |
| 2018 | 75 | 17 | 15.789474 | 0 | 4 | 114 |
| 2019 | 61 | 13 | 12.359551 | 0 | 4 | 89 |
| 2020 | 54 | 11 | 21.428571 | 0 | 1 | 84 |
| 2021 | 43 | 9 | 25.714286 | 0 | 0 | 70 |
| 2022 | 112 | 15 | 15.894040 | 0 | 0 | 151 |
| 2023 | 46 | 8 | 26.923077 | 1 | 2 | 78 |
| 2024 | 58 | 6 | 21.428571 | 0 | 2 | 84 |
| 2025 | 89 | 2 | 21.008403 | 1 | 2 | 119 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 36 | 68 | 7 | 0 | 1 | 44 |
| 2016 | 29 | 39 | 11 | 0 | 0 | 40 |
| 2017 | 50 | 25 | 11 | 0 | 2 | 63 |
| 2018 | 54 | 23 | 13 | 2 | 1 | 70 |
| 2019 | 64 | 16 | 21 | 0 | 1 | 86 |
| 2020 | 60 | 13 | 16 | 1 | 2 | 79 |
| 2021 | 55 | 10 | 21 | 0 | 3 | 79 |
| 2022 | 94 | 23 | 18 | 0 | 2 | 114 |
| 2023 | 32 | 7 | 21 | 0 | 0 | 53 |
| 2024 | 53 | 11 | 17 | 1 | 2 | 73 |
| 2025 | 52 | 8 | 31 | 0 | 2 | 85 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 36 | 68 | 6.250000 | 0 | 1 | 112 |
| 2016 | 29 | 39 | 13.924051 | 0 | 0 | 79 |
| 2017 | 50 | 25 | 12.500000 | 0 | 2 | 88 |
| 2018 | 54 | 23 | 13.978495 | 2 | 1 | 93 |
| 2019 | 64 | 16 | 20.588235 | 0 | 1 | 102 |
| 2020 | 60 | 13 | 17.391304 | 1 | 2 | 92 |
| 2021 | 55 | 10 | 23.595506 | 0 | 3 | 89 |
| 2022 | 94 | 23 | 13.138686 | 0 | 2 | 137 |
| 2023 | 32 | 7 | 35.000000 | 0 | 0 | 60 |
| 2024 | 53 | 11 | 20.238095 | 1 | 2 | 84 |
| 2025 | 52 | 8 | 33.333333 | 0 | 2 | 93 |
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[23], line 1 ----> 1 df_geral_separado NameError: name 'df_geral_separado' is not defined
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 93 | 84 | 15 | 0 | 4 | 112 |
| 2016 | 89 | 55 | 23 | 1 | 3 | 116 |
| 2017 | 128 | 57 | 23 | 0 | 2 | 153 |
| 2018 | 129 | 40 | 31 | 2 | 5 | 167 |
| 2019 | 125 | 29 | 32 | 0 | 5 | 162 |
| 2020 | 114 | 24 | 34 | 1 | 3 | 152 |
| 2021 | 98 | 19 | 39 | 0 | 3 | 140 |
| 2022 | 206 | 38 | 42 | 0 | 2 | 250 |
| 2023 | 78 | 15 | 42 | 1 | 2 | 123 |
| 2024 | 111 | 17 | 35 | 1 | 4 | 151 |
| 2025 | 141 | 10 | 56 | 1 | 4 | 202 |
| BRANCA | NAO DECLARADO | PP | INDÍGENA | AMARELA | Total | |
|---|---|---|---|---|---|---|
| 2015 | 93 | 84 | 13.392857 | 0 | 4 | 112 |
| 2016 | 89 | 55 | 19.827586 | 1 | 3 | 116 |
| 2017 | 128 | 57 | 15.032680 | 0 | 2 | 153 |
| 2018 | 129 | 40 | 18.562874 | 2 | 5 | 167 |
| 2019 | 125 | 29 | 19.753086 | 0 | 5 | 162 |
| 2020 | 114 | 24 | 22.368421 | 1 | 3 | 152 |
| 2021 | 98 | 19 | 27.857143 | 0 | 3 | 140 |
| 2022 | 206 | 38 | 16.800000 | 0 | 2 | 250 |
| 2023 | 78 | 15 | 34.146341 | 1 | 2 | 123 |
| 2024 | 111 | 17 | 23.178808 | 1 | 4 | 151 |
| 2025 | 141 | 10 | 27.722772 | 1 | 4 | 202 |
| Inscritos | |
|---|---|
| Brancos | 141 |
| Não Declarados | 10 |
| Pretos e Pardos | 56 |
| Indígenas | 1 |
| Amarelos | 4 |
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
| fluxes | reduced_costs | |
|---|---|---|
| NDPK5_fw | 0.003234 | 4.228388e-18 |
| SHK3Dr_fw | 0.045490 | 1.734723e-18 |
| NDPK6_fw | 0.003132 | -2.220446e-16 |
| NDPK8_fw | 0.003132 | 0.000000e+00 |
| DHORTS_fw | 0.000000 | -8.574123e-03 |
| ... | ... | ... |
| HYDFDN_bw | 0.000000 | -3.299734e-03 |
| PFOR_fw | 5.781830 | -4.336809e-19 |
| EX_h2_e_fw | 23.127319 | 0.000000e+00 |
| H2TPP_fw | 23.127319 | 0.000000e+00 |
| H2tex_fw | 23.127319 | 0.000000e+00 |
511 rows × 2 columns
INFO:root:Preparing strain design computation.
INFO:root: Using random seed 55661
INFO:root: Using gurobi for solving LPs during preprocessing.
WARNING:root: Removing reaction bounds when larger than the cobra-threshold of 1000.
INFO:root: FVA to identify blocked reactions and irreversibilities.
INFO:root: FVA(s) to identify essential reactions.
INFO:root:Preprocessing GPR rules (356 genes, 447 gpr rules).
INFO:root: Simplifyied to 190 genes and 183 gpr rules.
INFO:root: Extending metabolic network with gpr associations.
INFO:root:Compressing Network (836 reactions).
INFO:root: Removing blocked reactions.
INFO:root: Translating stoichiometric coefficients to rationals.
INFO:root: Removing conservation relations.
INFO:root: Compression 1: Applying compression from EFM-tool module.
INFO:root: Reduced to 472 reactions.
INFO:root: Compression 2: Lumping parallel reactions.
INFO:root: Reduced to 423 reactions.
INFO:root: Compression 3: Applying compression from EFM-tool module.
INFO:root: Reduced to 411 reactions.
INFO:root: Compression 4: Lumping parallel reactions.
INFO:root: Reduced to 410 reactions.
INFO:root: Compression 5: Applying compression from EFM-tool module.
INFO:root: Last step could not reduce size further (410 reactions).
INFO:root: Network compression completed. (4 compression iterations)
INFO:root: Translating stoichiometric coefficients back to float.
INFO:root: FVA(s) in compressed model to identify essential reactions.
INFO:root:Finished preprocessing:
INFO:root: Model size: 410 reactions, 215 metabolites
INFO:root: 286 targetable reactions
WARNING:root: Removing reaction bounds when larger than the cobra-threshold of 1000.
INFO:root:Constructing strain design MILP for solver: gurobi.
INFO:root: Bounding MILP.
INFO:root:Finding optimal strain designs ...
INFO:root:Strain design with cost 4.0: {'PFL_fw*G_b0902*pflB*R_g_b0902_and_g_b0903*R0_g_b0902_and_g_b0903_or_g_b0902_and_g_b3114*tdcE*R_g_b0902_and_g_b3114*R1_g_b0902_and_g_b0903_or_g_b0902_and_g_b3114': np.int64(-1), 'PDH_fw*aceE*aceF*R_g_b0114_and_g_b0115': np.int64(-1), 'PYRtex_bw': np.int64(-1), 'ldhA': np.int64(-1)}
INFO:root:Finished solving strain design MILP.
INFO:root:1 solutions to MILP found.
INFO:root: Decompressing.
INFO:root: Preparing (reaction-)phenotype prediction of gene intervention strategies.
INFO:root:9 solutions found.
One compressed solution with cost 4.0 found and expanded to 9 solutions in the uncompressed netork. Example intervention set: ['-PYRtex_bw', '-ldhA', '-PFL_fw', '-PDH_fw'] Knockout set on the reaction level: ['LDH_D_bw', 'PDH_fw', 'PYRtex_bw', 'PFL_fw', 'LDH_D_fw']
| fluxes | reduced_costs | |
|---|---|---|
| NDPK5_fw | 0.002968 | 1.084202e-19 |
| SHK3Dr_fw | 0.041747 | -1.734723e-18 |
| NDPK6_fw | 0.002875 | 2.220446e-16 |
| NDPK8_fw | 0.002875 | -1.387779e-17 |
| DHORTS_fw | 0.000000 | -8.178398e-03 |
| ... | ... | ... |
| HYDFDN_bw | 0.000000 | -3.147440e-03 |
| PFOR_fw | 7.170824 | 8.890458e-18 |
| EX_h2_e_fw | 28.683297 | 0.000000e+00 |
| H2TPP_fw | 28.683297 | 0.000000e+00 |
| H2tex_fw | 28.683297 | 0.000000e+00 |
511 rows × 2 columns
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29 Biomassa: 0.10320066433006311 Glucose consumption: 9.250127204153337
Read LP format model from file /tmp/tmpr5w2s5vi.lp Reading time = 0.00 seconds : 310 rows, 1022 columns, 4870 nonzeros Biomassa: 0.11970908215843598 H2: 23.127319034133066
Read LP format model from file /tmp/tmpagiipum_.lp Reading time = 0.00 seconds : 310 rows, 1022 columns, 4870 nonzeros Biomassa: 0.10986047739887762 H2: 28.6832967841541 Glucose consumption: 4.834670944898108
Read LP format model from file /tmp/tmp6jnt4x4i.lp Reading time = 0.00 seconds : 310 rows, 1022 columns, 4870 nonzeros Biomassa: None H2: 28.0
/home/cristian/.conda/envs/me25/lib/python3.12/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'. warn(f"Solver status is '{status}'.", UserWarning)
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
| fluxes | reduced_costs | |
|---|---|---|
| NDPK5_fw | 0.002788 | 1.235990e-17 |
| SHK3Dr_fw | 0.039217 | 3.469447e-18 |
| NDPK6_fw | 0.002700 | 2.220446e-16 |
| NDPK8_fw | 0.002700 | 0.000000e+00 |
| DHORTS_fw | 0.000000 | -7.631694e-03 |
| ... | ... | ... |
| 3OAR100_bw | 0.000000 | -3.431788e-03 |
| 3OAR180_bw | 0.000000 | 0.000000e+00 |
| ETOHtrpp_bw | 0.000000 | 0.000000e+00 |
| VPAMTr_bw | 0.060182 | 0.000000e+00 |
| enzyme_pool_supply | 0.281839 | 8.576399e-01 |
505 rows × 2 columns
np.float64(18.526949822952098)
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10.562660 | 1.216188 | 1.404998 | 0.483726 | 3.557768 | 3.074042 | 1.025892 | 0.542166 | 1.232227 | 1.232227 | 0.00000 | 1.478769 | 1.478769 | 0.741057 | 1.224783 | 0.000000 | 0.000000 | 16.679557 | 0.000000 |
| 1 | 9.670392 | 0.173741 | 2.871062 | 0.360367 | 0.867981 | 0.507615 | 0.360367 | 0.000000 | 0.360367 | 0.000000 | 0.09218 | 0.492923 | 0.582421 | 1.852641 | 1.849019 | 2.331004 | 4.195807 | 17.698335 | 0.005113 |
| 2 | 9.967815 | 1.389929 | 3.604094 | 1.183804 | 3.489335 | 2.305531 | 1.725970 | 0.542166 | 1.750182 | 0.566378 | 0.00000 | 2.454197 | 2.437544 | 1.131901 | 1.109411 | 0.932402 | 0.932402 | 19.835517 | 0.031742 |
| 3 | 11.157505 | 0.173741 | 2.504546 | 1.636492 | 3.173513 | 1.537021 | 3.805157 | 2.168665 | 1.798605 | 1.798605 | 0.00000 | 0.000000 | 0.000000 | 0.370528 | 0.608418 | 2.331004 | 0.932402 | 19.719451 | 0.000000 |
| 4 | 10.860082 | 0.694965 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 0.989498 | 0.542166 | 0.665849 | 0.603791 | 0.00000 | 0.030401 | 0.000000 | 2.259878 | 2.218822 | 2.331004 | 4.195807 | 22.306992 | 0.057947 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999995 | 10.860082 | 0.000000 | 0.671967 | 0.000000 | 0.000000 | 0.000000 | 3.352469 | 3.352469 | 0.099471 | 0.099471 | 0.00000 | 0.487509 | 0.487509 | 0.369804 | 0.369804 | 1.864803 | 2.536770 | 17.204439 | 0.000000 |
| 999996 | 8.480702 | 0.694965 | 2.138030 | 0.229919 | 2.151195 | 1.921276 | 1.725970 | 1.496051 | 1.798605 | 1.798605 | 0.00000 | 2.437544 | 2.437544 | 1.852641 | 2.548761 | 0.000000 | 0.466201 | 13.985866 | 0.000000 |
| 999997 | 8.778125 | 0.521223 | 3.237578 | 0.316884 | 1.469650 | 1.152766 | 1.725970 | 1.409086 | 0.883262 | 0.566378 | 0.00000 | 1.478769 | 1.445357 | 1.524338 | 1.479215 | 0.045123 | 0.000000 | 18.011220 | 0.063688 |
| 999998 | 9.967815 | 0.521223 | 1.771514 | 0.000000 | 1.636492 | 1.636492 | 1.084333 | 1.084333 | 1.232227 | 1.232227 | 0.00000 | 1.462527 | 1.462527 | 1.479215 | 1.479215 | 0.932402 | 2.703916 | 16.168368 | 0.000000 |
| 999999 | 10.562660 | 1.216188 | 0.671967 | 0.273401 | 2.194677 | 1.921276 | 3.352469 | 3.079067 | 0.556591 | 0.283189 | 0.00000 | 2.953313 | 2.925053 | 0.000000 | 0.000000 | 1.864803 | 1.864803 | 20.419944 | 0.053867 |
1000000 rows × 19 columns
| GLCptspp_fw | PDH_fw | PPC_fw | CS_fw | ACONTa_fw | ACONTa_bw | ACONTb_fw | ACONTb_bw | ICDHyr_fw | ICDHyr_bw | AKGDH_fw | SUCOAS_fw | SUCOAS_bw | FUM_fw | FUM_bw | MDH_fw | MDH_bw | Produção de NADH | Biomassa | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 682 | 10.265237 | 1.563670 | 2.871062 | 0.142954 | 0.527209 | 0.384255 | 1.183804 | 1.040850 | 0.426143 | 0.283189 | 0.000000 | 0.519631 | 0.487509 | 0.370528 | 0.369804 | 4.195807 | 4.195807 | 24.417125 | 0.061228 |
| 1251 | 10.860082 | 1.389929 | 3.604094 | 0.360367 | 0.360367 | 0.000000 | 2.810302 | 2.449936 | 0.643556 | 0.283189 | 0.000000 | 1.971692 | 1.945435 | 0.035460 | 0.000000 | 4.195807 | 4.195807 | 24.657655 | 0.050049 |
| 11359 | 11.157505 | 1.042447 | 3.237578 | 0.490815 | 0.490815 | 0.000000 | 2.117313 | 1.626499 | 1.340382 | 0.849567 | 0.061453 | 0.492923 | 0.526793 | 0.407055 | 0.369804 | 4.195807 | 4.195807 | 24.592242 | 0.052577 |
| 11831 | 10.562660 | 1.389929 | 2.871062 | 0.309687 | 0.483726 | 0.174040 | 0.851853 | 0.542166 | 0.592876 | 0.283189 | 0.245814 | 0.000000 | 0.214524 | 1.151667 | 1.109411 | 3.729606 | 3.729606 | 24.214517 | 0.059641 |
| 13024 | 10.562660 | 1.563670 | 3.604094 | 0.099471 | 0.099471 | 0.000000 | 1.183804 | 1.084333 | 0.949038 | 0.849567 | 0.000000 | 0.518868 | 0.487509 | 0.412153 | 0.369804 | 3.263405 | 3.263405 | 24.057084 | 0.059772 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 967341 | 11.157505 | 1.216188 | 3.604094 | 0.447332 | 0.483726 | 0.036394 | 1.531665 | 1.084333 | 0.730521 | 0.283189 | 0.153634 | 0.492923 | 0.614988 | 0.412436 | 0.369804 | 2.797205 | 3.263405 | 24.006515 | 0.060172 |
| 973918 | 10.562660 | 1.042447 | 3.237578 | 0.192313 | 0.960823 | 0.768510 | 0.192313 | 0.000000 | 0.192313 | 0.000000 | 0.122907 | 0.492923 | 0.581830 | 0.785524 | 0.739607 | 3.729606 | 4.195807 | 24.080630 | 0.064808 |
| 977070 | 10.265237 | 1.563670 | 3.237578 | 0.099471 | 1.252237 | 1.152766 | 0.099471 | 0.000000 | 0.949038 | 0.849567 | 0.000000 | 0.518514 | 0.487509 | 0.000000 | 0.000000 | 4.195807 | 4.195807 | 24.469771 | 0.059098 |
| 978778 | 11.157505 | 1.389929 | 3.604094 | 0.338586 | 0.867981 | 0.529395 | 1.965085 | 1.626499 | 0.338586 | 0.000000 | 0.276540 | 0.492923 | 0.739069 | 0.041047 | 0.000000 | 2.838252 | 2.797205 | 24.245067 | 0.057935 |
| 988765 | 10.265237 | 1.563670 | 2.504546 | 0.316884 | 0.701139 | 0.384255 | 0.316884 | 0.000000 | 0.600073 | 0.283189 | 0.245814 | 0.000000 | 0.213570 | 0.783151 | 0.739607 | 3.773150 | 3.729606 | 24.031348 | 0.061459 |
249 rows × 19 columns
<bound method IndexOpsMixin.tolist of Index(['GLCptspp_fw', 'PDH_fw', 'PPC_fw', 'CS_fw', 'ACONTa_fw', 'ACONTa_bw',
'ACONTb_fw', 'ACONTb_bw', 'ICDHyr_fw', 'ICDHyr_bw', 'AKGDH_fw',
'SUCOAS_fw', 'SUCOAS_bw', 'FUM_fw', 'FUM_bw', 'MDH_fw', 'MDH_bw',
'Produção de NADH', 'Biomassa'],
dtype='object')>| Produção de NADH | Biomassa | Melhores | |
|---|---|---|---|
| 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 |
| 4 | 1 | 0 | 0 |
| ... | ... | ... | ... |
| 999995 | 0 | 0 | 0 |
| 999996 | 0 | 0 | 0 |
| 999997 | 0 | 0 | 0 |
| 999998 | 0 | 0 | 0 |
| 999999 | 1 | 0 | 0 |
1000000 rows × 3 columns
| Produçao de NADH | Crescimento | |
|---|---|---|
| 0 | 1 | 1 |
| 1 | 0 | 0 |
| 2 | 1 | 1 |
| 3 | 0 | 0 |
| 4 | 0 | 0 |
| ... | ... | ... |
| 999995 | 1 | 1 |
| 999996 | 0 | 0 |
| 999997 | 0 | 0 |
| 999998 | 0 | 0 |
| 999999 | 0 | 0 |
1000000 rows × 2 columns
| Produçao de NADH | Crescimento | |
|---|---|---|
| 0 | 1 | 1 |
| 1 | 0 | 0 |
| 2 | 1 | 1 |
| 3 | 0 | 0 |
| 4 | 0 | 0 |
| ... | ... | ... |
| 999995 | 1 | 1 |
| 999996 | 0 | 0 |
| 999997 | 0 | 0 |
| 999998 | 0 | 0 |
| 999999 | 0 | 0 |
1000000 rows × 2 columns
R² Modelo NADH: 0.954452 R² Modelo Biomassa: 0.993984
Fitting 3 folds for each of 240 candidates, totalling 720 fits [CV 1/3] END max_depth=3, max_features=10, min_samples_leaf=1, n_estimators=100;, score=0.750 total time= 2.2min [CV 2/3] END max_depth=3, max_features=10, min_samples_leaf=1, n_estimators=100;, score=0.751 total time= 2.2min [CV 3/3] END max_depth=3, max_features=10, min_samples_leaf=1, n_estimators=100;, score=0.752 total time= 2.2min
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) Cell In[70], line 12 4 gr_space = { 5 'max_depth': [3,5,7,10], 6 'n_estimators': [100, 200, 300, 400, 500], 7 'max_features': [10, 20, 30 , 40], 8 'min_samples_leaf': [1, 2, 4] 9 } 11 grid = GridSearchCV(rf_grid, gr_space, cv = 3, scoring='accuracy', verbose = 3) ---> 12 model_grid = grid.fit(X_train_NADH, y_train_NADH) 14 print('Best hyperparameters are '+str(model_grid.best_params_)) 15 print('Best score is: ' + str(model_grid.best_score_)) File ~/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1389, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs) 1382 estimator._validate_params() 1384 with config_context( 1385 skip_parameter_validation=( 1386 prefer_skip_nested_validation or global_skip_validation 1387 ) 1388 ): -> 1389 return fit_method(estimator, *args, **kwargs) File ~/anaconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1024, in BaseSearchCV.fit(self, X, y, **params) 1018 results = self._format_results( 1019 all_candidate_params, n_splits, all_out, all_more_results 1020 ) 1022 return results -> 1024 self._run_search(evaluate_candidates) 1026 # multimetric is determined here because in the case of a callable 1027 # self.scoring the return type is only known after calling 1028 first_test_score = all_out[0]["test_scores"] File ~/anaconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1571, in GridSearchCV._run_search(self, evaluate_candidates) 1569 def _run_search(self, evaluate_candidates): 1570 """Search all candidates in param_grid""" -> 1571 evaluate_candidates(ParameterGrid(self.param_grid)) File ~/anaconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:970, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results) 962 if self.verbose > 0: 963 print( 964 "Fitting {0} folds for each of {1} candidates," 965 " totalling {2} fits".format( 966 n_splits, n_candidates, n_candidates * n_splits 967 ) 968 ) --> 970 out = parallel( 971 delayed(_fit_and_score)( 972 clone(base_estimator), 973 X, 974 y, 975 train=train, 976 test=test, 977 parameters=parameters, 978 split_progress=(split_idx, n_splits), 979 candidate_progress=(cand_idx, n_candidates), 980 **fit_and_score_kwargs, 981 ) 982 for (cand_idx, parameters), (split_idx, (train, test)) in product( 983 enumerate(candidate_params), 984 enumerate(cv.split(X, y, **routed_params.splitter.split)), 985 ) 986 ) 988 if len(out) < 1: 989 raise ValueError( 990 "No fits were performed. " 991 "Was the CV iterator empty? " 992 "Were there no candidates?" 993 ) File ~/anaconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:77, in Parallel.__call__(self, iterable) 72 config = get_config() 73 iterable_with_config = ( 74 (_with_config(delayed_func, config), args, kwargs) 75 for delayed_func, args, kwargs in iterable 76 ) ---> 77 return super().__call__(iterable_with_config) File ~/anaconda3/lib/python3.12/site-packages/joblib/parallel.py:1918, in Parallel.__call__(self, iterable) 1916 output = self._get_sequential_output(iterable) 1917 next(output) -> 1918 return output if self.return_generator else list(output) 1920 # Let's create an ID that uniquely identifies the current call. If the 1921 # call is interrupted early and that the same instance is immediately 1922 # re-used, this id will be used to prevent workers that were 1923 # concurrently finalizing a task from the previous call to run the 1924 # callback. 1925 with self._lock: File ~/anaconda3/lib/python3.12/site-packages/joblib/parallel.py:1847, in Parallel._get_sequential_output(self, iterable) 1845 self.n_dispatched_batches += 1 1846 self.n_dispatched_tasks += 1 -> 1847 res = func(*args, **kwargs) 1848 self.n_completed_tasks += 1 1849 self.print_progress() File ~/anaconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:139, in _FuncWrapper.__call__(self, *args, **kwargs) 137 config = {} 138 with config_context(**config): --> 139 return self.function(*args, **kwargs) File ~/anaconda3/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:866, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, score_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score) 864 estimator.fit(X_train, **fit_params) 865 else: --> 866 estimator.fit(X_train, y_train, **fit_params) 868 except Exception: 869 # Note fit time as time until error 870 fit_time = time.time() - start_time File ~/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1389, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs) 1382 estimator._validate_params() 1384 with config_context( 1385 skip_parameter_validation=( 1386 prefer_skip_nested_validation or global_skip_validation 1387 ) 1388 ): -> 1389 return fit_method(estimator, *args, **kwargs) File ~/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_forest.py:487, in BaseForest.fit(self, X, y, sample_weight) 476 trees = [ 477 self._make_estimator(append=False, random_state=random_state) 478 for i in range(n_more_estimators) 479 ] 481 # Parallel loop: we prefer the threading backend as the Cython code 482 # for fitting the trees is internally releasing the Python GIL 483 # making threading more efficient than multiprocessing in 484 # that case. However, for joblib 0.12+ we respect any 485 # parallel_backend contexts set at a higher level, 486 # since correctness does not rely on using threads. --> 487 trees = Parallel( 488 n_jobs=self.n_jobs, 489 verbose=self.verbose, 490 prefer="threads", 491 )( 492 delayed(_parallel_build_trees)( 493 t, 494 self.bootstrap, 495 X, 496 y, 497 sample_weight, 498 i, 499 len(trees), 500 verbose=self.verbose, 501 class_weight=self.class_weight, 502 n_samples_bootstrap=n_samples_bootstrap, 503 missing_values_in_feature_mask=missing_values_in_feature_mask, 504 ) 505 for i, t in enumerate(trees) 506 ) 508 # Collect newly grown trees 509 self.estimators_.extend(trees) File ~/anaconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:77, in Parallel.__call__(self, iterable) 72 config = get_config() 73 iterable_with_config = ( 74 (_with_config(delayed_func, config), args, kwargs) 75 for delayed_func, args, kwargs in iterable 76 ) ---> 77 return super().__call__(iterable_with_config) File ~/anaconda3/lib/python3.12/site-packages/joblib/parallel.py:1918, in Parallel.__call__(self, iterable) 1916 output = self._get_sequential_output(iterable) 1917 next(output) -> 1918 return output if self.return_generator else list(output) 1920 # Let's create an ID that uniquely identifies the current call. If the 1921 # call is interrupted early and that the same instance is immediately 1922 # re-used, this id will be used to prevent workers that were 1923 # concurrently finalizing a task from the previous call to run the 1924 # callback. 1925 with self._lock: File ~/anaconda3/lib/python3.12/site-packages/joblib/parallel.py:1847, in Parallel._get_sequential_output(self, iterable) 1845 self.n_dispatched_batches += 1 1846 self.n_dispatched_tasks += 1 -> 1847 res = func(*args, **kwargs) 1848 self.n_completed_tasks += 1 1849 self.print_progress() File ~/anaconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:139, in _FuncWrapper.__call__(self, *args, **kwargs) 137 config = {} 138 with config_context(**config): --> 139 return self.function(*args, **kwargs) File ~/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_forest.py:189, in _parallel_build_trees(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap, missing_values_in_feature_mask) 186 elif class_weight == "balanced_subsample": 187 curr_sample_weight *= compute_sample_weight("balanced", y, indices=indices) --> 189 tree._fit( 190 X, 191 y, 192 sample_weight=curr_sample_weight, 193 check_input=False, 194 missing_values_in_feature_mask=missing_values_in_feature_mask, 195 ) 196 else: 197 tree._fit( 198 X, 199 y, (...) 202 missing_values_in_feature_mask=missing_values_in_feature_mask, 203 ) File ~/anaconda3/lib/python3.12/site-packages/sklearn/tree/_classes.py:472, in BaseDecisionTree._fit(self, X, y, sample_weight, check_input, missing_values_in_feature_mask) 461 else: 462 builder = BestFirstTreeBuilder( 463 splitter, 464 min_samples_split, (...) 469 self.min_impurity_decrease, 470 ) --> 472 builder.build(self.tree_, X, y, sample_weight, missing_values_in_feature_mask) 474 if self.n_outputs_ == 1 and is_classifier(self): 475 self.n_classes_ = self.n_classes_[0] KeyboardInterrupt:
R² Modelo NADH: 0.8695652173913043 R² Modelo Biomassa: 0.8913043478260869
Text(0, 0.5, 'Produçao Real de NADH')
Text(0, 0.5, 'Produçao Real de Biomassa')
Text(0.5, 1.0, 'Curva ROC AUC')
- Melhores Fluxos Interativo.ipynb
- Grafico.ipynb
- Analises iCH360.ipynb
- Growth_Couple_iCH360.ipynb
- Final.ipynb
- Clusters.ipynb
- Escher.ipynb
- IC-PIBIC_pt2.ipynb
/home/cristian/.conda/envs/escher/bin/python
Set parameter Username Set parameter LicenseID to value 2644080 Academic license - for non-commercial use only - expires 2026-03-29
PDH¶
Read LP format model from file /tmp/tmptzywuw6q.lp Reading time = 0.01 seconds : 1805 rows, 5166 columns, 20366 nonzeros